Studies Of Galaxy Evolution Research Articles

CEERS Key Paper. IX. Identifying Galaxy Mergers in CEERS NIRCam Images Using Random Forests and Convolutional Neural Networks

A crucial yet challenging task in galaxy evolution studies is the identification of distant merging galaxies, a task that suffers from a variety of issues ranging from telescope sensitivities and limitations to the inherently chaotic morphologies of young galaxies. In this paper, we use random forests and convolutional neural networks to identify high-redshift JWST Cosmic Evolution Early Release Science Survey (CEERS) galaxy mergers. We train these algorithms on simulated 3 < z < 5 CEERS galaxies created from the IllustrisTNG subhalo morphologies and the Santa Cruz SAM light cone. We apply our models to observed CEERS galaxies at 3 < z < 5. We find that our models correctly classify ∼60%–70% of simulated merging and nonmerging galaxies; better performance on the merger class comes at the expense of misclassifying more nonmergers. We could achieve more accurate classifications, as well as test for a dependency on physical parameters such as gas fraction, mass ratio, and relative orbits, by curating larger training sets. When applied to real CEERS galaxies using visual classifications as ground truth, the random forests correctly classified 40%–60% of mergers and nonmergers at 3 < z < 4 but tended to classify most objects as nonmergers at 4 < z < 5 (misclassifying ∼70% of visually classified mergers). On the other hand, the CNNs tended to classify most objects as mergers across all redshifts (misclassifying 80%–90% of visually classified nonmergers). We investigate what features the models find most useful, as well as the characteristics of false positives and false negatives, and also calculate merger rates derived from the identifications made by the models.

A Systematic Search of Distant Superclusters with the Subaru Hyper Suprime-Cam Survey

Superclusters, encompassing environments across a wide range of overdensities, can be regarded as unique laboratories for studying galaxy evolution. Although numerous supercluster catalogs have been published, none of them goes beyond redshift z = 0.7. In this work, we adopt a physically motivated supercluster definition, requiring that superclusters should eventually collapse even in the presence of dark energy. Applying a friends-of-friends (FoF) algorithm to the CAMIRA cluster sample constructed using the Subaru Hyper Suprime-Cam survey data, we have conducted the first systematic search for superclusters at z = 0.5–1.0 and identified 673 supercluster candidates over an area of 1027 deg2. The FoF algorithm is calibrated by evolving N-body simulations to the far future to ensure high purity. We found that these high-z superclusters are mainly composed of two to four clusters, suggesting the limit of gravitationally bound structures in the younger Universe. In addition, we studied the properties of the clusters and brightest cluster galaxies (BCGs) residing in different large-scale environments. We found that clusters associated with superclusters are typically richer, but no apparent dependence of the BCG properties on large-scale structures is found. We also compared the abundance of observed superclusters with mock superclusters extracted from halo light cones, finding that photometric redshift uncertainty is a limiting factor in the performance of superclusters detection.

The tight correlation between PAH and CO emission from z ∼ 0 to 4

Aims. The cold molecular gas mass is one of the crucial, yet challenging, parameters in galaxy evolution studies. Here, we introduce a new calibration and a method for estimating molecular gas masses using mid-infrared (MIR) photometry. This topic is timely as the James Webb Space Telescope (JWST) now allows us to detect the MIR emission of typical main-sequence galaxies across a wide range of masses and star formation rates with modest time investments. Additionally, this Letter highlights the strong synergy between ALMA and JWST for studies of dust and gas at cosmic noon. Methods. We combined a sample of 14 main-sequence galaxies at z = 1 − 3 with robust CO detections and multi-band MIR photometry, along with a literature sample at z = 0 − 4 with CO and polycyclic aromatic hydrocarbon (PAH) spectroscopy, to study the relationship between PAH, CO(1–0), and total IR luminosities. PAH luminosities are derived by modelling a wealth of rest-frame UV to sub-millimetre data. The new z = 1 − 3 sample extends previous high-z studies to PAH and CO luminosities that are about an order of magnitude lower, into the regime of local starbursts, for the first time. Results. The PAH-to-CO luminosity ratio remains constant across a wide range of luminosities, for various galaxy types, and throughout the explored redshift range. In contrast, the PAH-to-IR and CO-to-IR luminosity ratios deviate from a constant value at high IR luminosities. The intrinsic scatter in the L(PAH)–L′(CO) relation is 0.21 dex, with a median of 1.40 and a power-law slope of 1.07 ± 0.04. Both the PAH–IR and CO–IR relations are sub-linear. Given the tight and uniform PAH–CO relation over ∼3 orders of magnitude, we provide a recipe for estimating the cold molecular gas mass of galaxies from PAH luminosities, with a PAH-to-molecular gas conversion factor of αPAH7.7 = (3.08 ± 1.08)(4.3/αCO) M⊙/L⊙. This method opens a new window to explore the gas content of galaxies beyond the local Universe using multi-wavelength JWST/MIRI imaging.

Carbon envelopes around merging galaxies at z ∼ 4.5

Context. Galaxies evolve through a dynamic exchange of material with their immediate surrounding environment, the so-called circumgalactic medium (CGM). Understanding the physics of gas flows and the nature of the CGM is fundamental to studying galaxy evolution, especially at 4 ≤ z ≤ 6 (i.e., after the Epoch of Reionization) when galaxies rapidly assembled their masses and reached their chemical maturity. Galactic outflows are predicted to enrich the CGM with metals, although it has also been suggested that gas stripping in systems undergoing a major merger may play a role. Aims. In this work, we explore the metal enrichment of the medium around merging galaxies at z ∼ 4.5, observed by the ALMA Large Program to INvestigate [CII] at Early times (ALPINE). To do so, we study the nature of the [CII] 158 μm emission in the CGM around these systems, using simulations to help disentangle the mechanisms contributing to the CGM metal pollution. Methods. By adopting an updated classification of major merger systems in the ALPINE survey, we selected and analyzed merging galaxies whose components can be spatially and/or spectrally resolved in a robust way. This makes it possible to distinguish between the [CII] emission coming from the single components of the system and that coming from the system as a whole. We also made use of the dustyGadget cosmological simulation to select synthetic analogs of observed galaxies and guide the interpretation of the observational results. Results. We find a large diffuse [CII] envelope (≳20 kpc) embedding all the merging systems, with at least 25% of the total [CII] emission coming from the medium between the galaxies. Using predictions from dustyGadget, we suggest that this emission has a multi-fold nature, with dynamical interactions between galaxies playing a major role in stripping the gas and enriching the medium with heavy elements.

Identifying Mergers in the Legacy Surveys with Few-shot Learning

Galaxy mergers exert a pivotal influence on the evolutionary trajectory of galaxies and the expansive development of cosmic structures. The primary challenge encountered in machine learning–based identification of merging galaxies arises from the scarcity of meticulously labeled data sets specifically dedicated to merging galaxies. In this paper, we propose a novel framework utilizing few-shot learning techniques to identify galaxy mergers in the Legacy Surveys. Few-shot learning enables effective classification of merging galaxies even when confronted with limited labeled training samples. We employ a deep convolutional neural network architecture trained on data sets sampled from Galaxy Zoo Decals to learn essential features and generalize to new instances. Our experimental results demonstrate the efficacy of our approach, achieving high accuracy and precision in identifying galaxy mergers with few labeled training samples. Furthermore, we investigate the impact of various factors, such as the number of training samples and network architectures, on the performance of the few-shot learning model. The proposed methodology offers a promising avenue for automating the identification of galaxy mergers in large-scale surveys, facilitating the comprehensive study of galaxy evolution and structure formation. In pursuit of identifying galaxy mergers, our methodology is applied to analyze the Data Release 9 of the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys. As a result, we have unveiled an extensive catalog encompassing 648,183 galaxy merger candidates. We publicly release the catalog alongside this paper.

The HYENAS project: a prediction for the X-ray undetected galaxy groups

Abstract Galaxy groups contain the majority of bound mass with a significant portion of baryons due to the combination of halo mass and abundance (Cui 2024). Hence they serve as a crucial missing piece in the puzzle of galaxy formation and the evolution of large-scale structures in the Universe. In observations, mass-complete group catalogues are normally derived from galaxy redshift surveys detected through various three-dimensional group-finding algorithms. Confirming the reality of such groups, particularly in the X-rays, is critical for ensuring robust studies of galaxy evolution in these environments. Recent works have reported numerous optical groups that are X-ray undetected (see, e.g. Popesso et al. 2024), sparking debates regarding the reasons for the unexpectedly low hot gas fraction in galaxy groups. To address this issue, we utilise zoomed-in simulations of galaxy groups from the novel Hyenas project to explore the range of hot gas fractions within galaxy groups and investigate the intrinsic factors behind the observed variability in X-ray emission. We find that the halo formation time can play a critical role – we see that groups in halos that formed earlier exhibit up to an order of magnitude brighter X-ray luminosities compared to those formed later. This suggests that undetected X-ray groups are preferentially late-formed halos and highlights the connection between gas fraction and halo formation time in galaxy groups. Accounting for these biases in galaxy group identification is essential for advancing our understanding of galaxy formation and achieving precision in cosmological studies.

Euclid preparation

The Euclid mission is expected to image millions of galaxies at high resolution, providing an extensive dataset with which to study galaxy evolution. Because galaxy morphology is both a fundamental parameter and one that is hard to determine for large samples, we investigate the application of deep learning in predicting the detailed morphologies of galaxies in Euclid using Zoobot, a convolutional neural network pretrained with 450 000 galaxies from the Galaxy Zoo project. We adapted Zoobot for use with emulated Euclid images generated based on Hubble Space Telescope COSMOS images and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We experimented with different numbers of galaxies and various magnitude cuts during the training process. We demonstrate that the trained Zoobot model successfully measures detailed galaxy morphology in emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features, such as spiral arms, clumps, bars, discs, and central bulges. When compared to volunteer classifications, Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy of above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes, such as disc or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. On the other hand, for more detailed structures and complex tasks, such as detecting and counting spiral arms or clumps, the deviations are slightly higher, of namely around 12% with 60 000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowd sourcing. We estimate that, with our model, the detailed morphology of approximately 800 million galaxies of the Euclid Wide Survey could be reliably measured and that approximately 230 million of these galaxies would display features. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images.

A Survey of Galaxy Pairs in the SDSS Photometric Images based on Faster-RCNN

Galaxy pairs hold significant importance in understanding the evolution of galaxies, and the extensive search for a large sample of galaxy pairs is meaningful. In this article, we develop a deep learning-based approach for the search of galaxy pairs and conduct a comprehensive search on Sloan Digital Sky Survey (SDSS) images. In nine million photometric images, 17,965 physical galaxy pairs with spectral or photometric redshifts are detected. Four sets of results are provided, including physical pairs determined by two spectral redshifts, two photometric redshifts, one spectral redshift, and one photometric redshift, and visual irregular pairs that have no precise redshift information but can be inferred as physical galaxy pairs based on the morphological changes. Then their morphological and physical characteristics are explored, the redshifts of most targets are around 0.1, and as the redshift difference between two galaxies increases, the number of galaxy pairs gradually reduces. The distributions of star formation rate (SFR) are not the same for different morphologies of galaxy pairs, irregular pairs have higher SFR than the other three types, and statistics indicate that the SFR of galaxies depends on both nearby galaxies and internal properties. Color and stellar mass are also key properties of galaxies which can reflect the status of galaxy pairs. Compared to other surveys, a greater number of galaxy pair targets are detected, and this is also the first extensive detection of galaxy pairs in SDSS images using photometric redshifts. These galaxy pair samples can greatly aid in the study of galaxy evolution.

The MAGPI survey: The interdependence of the mass, star formation rate, and metallicity in galaxies at z~0.3

Star formation rates (SFRs), gas-phase metallicities, and stellar masses are crucial for studying galaxy evolution. The different relations resulting from these properties give insights into the complex interplay of gas inside galaxies and their evolutionary trajectory and current characteristics. We aim to characterize these relations at $z 0.3$, corresponding to a 3-4 Gyr lookback time, to gather insight into the galaxies' redshift evolution. We utilized optical integral field spectroscopy data from 65 emission-line galaxies from the MUSE large program MAGPI at a redshift of $0.28&lt;z&lt;0.35$ (average redshift of $z 0.3$) and spanning a total stellar mass range of $8.2&lt; /M_ odot ) &lt; 11.4$. We measured emission line fluxes and stellar masses, allowing us to determine spatially resolved SFRs, gas-phase metallicities, and stellar mass surface densities. We derived the resolved star formation main sequence (rSFMS), resolved mass metallicity relation (rMZR), and resolved fundamental metallicity relation (rFMR) at $z 0.3$, and compared them to results for the local Universe. We find a relatively shallow rSFMS slope of $ 0.014$ compared to the expected slope at this redshift for an ordinary least square (OLS) fitting routine. For an orthogonal distance regression (ODR) routine, a much steeper slope of $ 0.022$ is measured. We confirm the existence of an rMZR at $z 0.3$ with an average metallicity located $ 0.03$ dex above the local Universe's metallicity. Via partial correlation coefficients, evidence is found that the local metallicity is predominantly determined by the stellar mass surface density and has a weak secondary (inverse) dependence on the SFR surface density $ SFR $. Additionally, a significant dependence of the local metallicity on the total stellar mass $M_ $ is found. Furthermore, we find that the stellar mass surface density $ $ and $M_ $ have a significant influence in determining the strength with which $ SFR $ correlates with the local metallicity. We observe that at lower stellar masses, there is a tighter correlation between $ SFR $ and the gas-phase metallicity, resulting in a more pronounced rFMR.

The baryon cycle in modern cosmological hydrodynamical simulations

ABSTRACT In recent years, cosmological hydrodynamical simulations have proven their utility as key interpretative tools in the study of galaxy formation and evolution. In this work, we present a comparative analysis of the baryon cycle in three publicly available, leading cosmological simulation suites: EAGLE, IllustrisTNG, and SIMBA. While these simulations broadly agree in terms of their predictions for the stellar mass content and star formation rates of galaxies at $z\approx 0$, they achieve this result for markedly different reasons. In EAGLE and SIMBA, we demonstrate that at low halo masses ($M_{\rm 200c}\lesssim 10^{11.5}\, \mathrm{M}_{\odot }$), stellar feedback (SF)-driven outflows can reach far beyond the scale of the halo, extending up to $2\!-\!3\times R_{\rm 200c}$. In contrast, in TNG, SF-driven outflows, while stronger at the scale of the interstellar medium, recycle within the circumgalactic medium (within $R_{\rm 200c}$). We find that active galactic nucleus (AGN)-driven outflows in SIMBA are notably potent, reaching several times $R_{\rm 200c}$ even at halo masses up to $M_{\rm 200c}\approx 10^{13.5}\, \mathrm{M}_{\odot }$. In both TNG and EAGLE, AGN feedback can eject gas beyond $R_{\rm 200c}$ at this mass scale, but seldom beyond $2\!-\!3\times R_{\rm 200c}$. We find that the scale of feedback-driven outflows can be directly linked with the prevention of cosmological inflow, as well as the total baryon fraction of haloes within $R_{\rm 200c}$. This work lays the foundation to develop targeted observational tests that can discriminate between feedback scenarios, and inform subgrid feedback models in the next generation of simulations.

Atacama Large Aperture Submillimeter Telescope (AtLAST) science: The hidden circumgalactic medium.

Our knowledge of galaxy formation and evolution has incredibly progressed through multi-wavelength observational constraints of the interstellar medium (ISM) of galaxies at all cosmic epochs. However, little is known about the physical properties of the more diffuse and lower surface brightness reservoir of gas and dust that extends beyond ISM scales and fills dark matter haloes of galaxies up to their virial radii, the circumgalactic medium (CGM). New theoretical studies increasingly stress the relevance of the latter for understanding the feedback and feeding mechanisms that shape galaxies across cosmic times, whose cumulative effects leave clear imprints into the CGM. Recent studies are showing that a - so far unconstrained - fraction of the CGM mass may reside in the cold ( T < 10 4 K) molecular and atomic phase, especially in high-redshift dense environments. These gas phases, together with the warmer ionised phase, can be studied in galaxies from z ∼ 0 to z ∼ 10 through bright far-infrared and sub-millimeter emission lines such as [C ii] 158 µm, [O iii] 88 µm, [C I] 609 µm, [C i] 370 µm, and the rotational transitions of CO. Imaging such hidden cold CGM can lead to a breakthrough in galaxy evolution studies but requires a new facility with the specifications of the proposed Atacama Large Aperture Submillimeter Telescope (AtLAST). In this paper, we use theoretical and empirical arguments to motivate future ambitious CGM observations with AtLAST and describe the technical requirements needed for the telescope and its instrumentation to perform such science.

CAVITY, Calar Alto Void Integral-field Treasury surveY and project extension

We have learnt in the last decades that the majority of galaxies belong to high density regions interconnected in a sponge-like fashion. This large-scale structure is characterised by clusters, filaments, and walls, where most galaxies concentrate, but also under-dense regions called voids. The void regions and the galaxies within represent an ideal place for the study of galaxy formation and evolution, as they are largely unaffected by the complex physical processes that transform galaxies in high-density environments. The void galaxies may hold the key to answer current challenges to the Lambda CDM paradigm as well. The CAVITY survey is a Legacy project approved by the Calar Alto Observatory to obtain spatially resolved spectroscopic information of $ void galaxies in the Local Universe (0.005 &lt; z &lt; 0.050), covering -17.0 to -21.5 in $ r$ band absolute magnitude. It officially started in January 2021 and has been awarded 110 useful dark observing nights at the 3.5 m telescope using the PMAS spectrograph. Complementary follow-up projects, including deep optical imaging, integrated as well as resolved CO data, and integrated HI spectra, have joined the PMAS observations and naturally complete the scientific aim of characterising galaxies in cosmic voids. The extension data has been named CAVITY+. The data will be available to the whole community in different data releases, the first of which is planned for July 2024, and it will provide the community with PMAS datacubes for around 100 void galaxies through a user friendly and well documented database platform. Here, we present the survey, sample selection, data reduction, quality control schemes, science goals, and some examples of the scientific power of the CAVITY and CAVITY+ data.

ComPACT: combined Atacama Cosmology Telescope + Planck galaxy cluster catalogue

ABSTRACT Galaxy clusters are the most massive gravitationally bound systems consisting of dark matter, hot baryonic gas, and stars. They play an important role in observational cosmology and galaxy evolution studies. We develop a deep learning model for segmentation of Sunyaev–Zeldovich (SZ) signal on Atacama Cosmology Telescope (ACT) + Planck intensity maps and construct a pipeline for microwave cluster detection in the ACT footprint. The proposed model allows us to identify previously unknown galaxy clusters, i.e. it is capable of detecting SZ sources below the detection threshold adopted in the published galaxy clusters catalogues [such as ACT DR5 and Planck Sunyaev–Zeldovich 2 (PSZ2)]. In this paper, we use the derived SZ signal map to considerably improve a cluster purity in the extended catalogue of Sunyaev–Zeldovich objects from Planck data (SZcat) in the ACT footprint. From SZcat, we create a new microwave galaxy cluster catalogue (ComPACT), which includes 2962 SZ objects with cluster purity conservatively estimated as ≳74–84 per cent. We categorize objects in the catalogue into three categories, based on their cluster reliability. Within the ComPACT catalogue, there are ≳977 new clusters with respect to the ACT DR5 and PSZ2 catalogues.

SMART: Spectral energy distributions Markov chain Analysis with Radiative Transfer models

Abstract In this paper we present the publicly available open-source spectral energy distribution (SED) fitting code SMART (Spectral energy distributions Markov chain Analysis with Radiative Transfer models). Implementing a Bayesian Markov chain Monte Carlo (MCMC) method, SMART fits the ultraviolet to millimetre SEDs of galaxies exclusively with radiative transfer models that currently constitute four types of pre-computed libraries, which describe the starburst, active galactic nucleus (AGN) torus, host galaxy and polar dust components. An important novelty of SMART is that, although it fits SEDs exclusively with radiative transfer models, it takes comparable time to popular energy balance methods to run. Here we describe the key features of SMART and test it by fitting the multi-wavelength SEDs of the 42 local ultraluminous infrared galaxies (ULIRGs) that constitute the HERschel Ultraluminous Infrared Galaxy Survey (HERUS) sample. The Spitzer spectroscopy data of the HERUS ULIRGs are included in the fitting at a spectral resolution, which is matched to that of the radiative transfer models. We also present other results that highlight the performance and versatility of SMART. SMART promises to be a useful tool for studying galaxy evolution in the James Webb Space Telescope (JWST) era. SMART is developed in Python and is available at https://github.com/ch-var/SMART.git.

Emulating the interstellar medium chemistry with neural operators

Context. The study of galaxy formation and evolution critically depends on our understanding of the complex photo-chemical processes that govern the evolution and thermodynamics of the interstellar medium (ISM). In a computational sense, resolving the chemistry is among the weightiest tasks in cosmological and astrophysical simulations. Aims. Astrophysical simulations can include photo-chemical models that allow for a wide range of densities (n), abundances of different species (ni/n) and temperature (T), and plausible evolution scenarios of the ISM under the action of a radiation field (F) with different spectral shapes and intensities. The evolution of such a non-equilibrium photo-chemical network relies on implicit, precise, computationally costly, ordinary differential equations (ODE) solvers. Here, we aim to substitute such procedural solvers with fast, pre-trained emulators based on neural operators. Methods. We emulated a non-equilibrium chemical network up to H2 formation (9 species, 52 reactions) by adopting the DeepONet formalism, namely: by splitting the ODE solver operator that maps the initial conditions and time evolution into a tensor product of two neural networks (named branch and trunk). We used KROME to generate a training set, spanning −2 &lt; log(n/cm−3) ≤ 3.5, log(20) ≤ log(T/K) ≤ 5.5, −6 ≤ log(ni/n) &lt; 0, and adopting an incident radiation field, F, sampled in 10 energy bins with a continuity prior. We separately trained the solver for T and each ni for ≃4.34 GPUhrs. Results. Compared with the reference solutions obtained by KROME for single-zone models, the typical precision obtained is of the order of 10−2, that is, it is 10 times better when using a training that is 40 times less costly, with respect to previous emulators that only considered a fixed F. DeepONet also performs well for T and ni outside the range of the training sample. Furthermore, the emulator aptly reproduces the ion and temperature profiles of photo dissociation regions as well; namely, by giving errors that are comparable to the typical difference between various photo-ionization codes. The present model achieves a speed-up of a factor of 128× with respect to stiff ODE solvers. Conclusions. Our neural emulator represents a significant leap forward in the modelling of ISM chemistry, offering a good balance of precision, versatility, and computational efficiency. Nevertheless, further work is required to address the challenges represented by the extrapolation beyond the training time domain and the removal of potential outliers.

ALMA Lensing Cluster Survey: Full Spectral Energy Distribution Analysis of z ∼ 0.5–6 Lensed Galaxies Detected with millimeter Observations

Sub/millimeter galaxies are a key population for the study of galaxy evolution because the majority of star formation at high redshifts occurred in galaxies deeply embedded in dust. To search for this population, we have performed an extensive survey with Atacama Large Millimeter/submillimeter Array (ALMA), called the ALMA Lensing Cluster Survey (ALCS). This survey covers 133 arcmin2 area and securely detects 180 sources at z ∼ 0.5–6 with a flux limit of ∼0.2 mJy at 1.2 mm. Here, we report the results of multiwavelength spectral energy distribution analysis of the whole ALCS sample, utilizing the observed-frame UV to millimeter photometry. We find that the majority of the ALCS sources lie on the star-forming main sequence, with a smaller fraction showing intense starburst activities. The ALCS sample contains high infrared-excess sources ( IRX=log(Ldust/LUV)>1 ), including two extremely dust-obscured galaxies (IRX > 5). We also confirm that the ALCS sample probes a broader range in lower dust mass than conventional submillimeter galaxy samples in the same redshift range. We identify six heavily obscured active galactic nucleus (AGN) candidates that are not detected in the archival Chandra data in addition to the three X-ray AGNs reported by Uematsu et al. (2023). The inferred AGN luminosity density shows a possible excess at z = 2–3 compared with that determined from X-ray surveys below 10 keV.

Assessment of SDSS-derived Galaxy Morphologies Using HST Imaging

The Sloan Digital Sky Survey (SDSS) was foundational to the study of galaxy evolution, having revealed the bimodality of galaxies and the relationship between their structure and star-forming activity. However, ground-based optical surveys like SDSS are limited in resolution and depth, which may lead to biases or poor quality in the derived morphological properties, potentially impacting our understanding of how and why galaxies cease their star formation (quench). We use archival Hubble Space Telescope (HST) imaging of ∼2000 SDSS objects to assess the reliability of SDSS-derived morphologies, taking advantage of both SDSS statistical samples and HST’s superior resolution and sensitivity. Single Sérsic fitting and bulge-disk decomposition is performed on HST images for direct comparison with SDSS results. Of the three catalogs of SDSS-derived morphologies considered, none is significantly more accurate or precise than the others. For disk-dominated galaxies (n < 2.5), global Sérsic indices (n) from Meert et al. (hereafter, M15) are preferred. For bulge-dominated galaxies (n > 2.5), Simard et al. (hereafter, S11) and M15 overestimate n by ∼20%, and derived global n from Blanton et al. are preferred. We provide an empirical relation to correct S11 global Sérsic indices. Global R eff from S11 overestimates R eff for the largest galaxies by 0.1 dex. Despite these moderate biases, SDSS-derived single-component parameters are generally significantly more robust than SDSS-derived two-component parameters. The bulge Sérsic index (n bulge) cannot be reliably constrained from SDSS imaging at all, whereas the bulge-to-total (B/T) ratio can be inferred from SDSS (provided that n bulge = 4 is enforced) but has a large random error of ∼0.2.

A 600 m2 array of 6.5 m telescopes at the lunar pole.

The proposed lunar telescope for optical and infrared astronomy aims at very large aperture, 600 m2, at a fundable cost. It comprises an array of 18 separate telescopes, each of 6.5 m aperture. The 200 m diameter array will be located within 1/2° (15 km) of a lunar pole on approximately level ground, with a perimeter screen deployed to provide shade and cooling to cryogenic temperature. The 500 m diameter screen will allow unobscured access down to 8° elevation. All 18 telescopes will reflect light into a central beam combiner to form a single image covering wavelengths from 0.4 µm to 10 µm. The initial instrument complement will include high-resolution and multi-object spectrographs to exploit the single combined field of view of two arcminute diameter, with the diffraction limited resolution of 6.5 m aperture. Scientific applications include the search for molecular biosignatures in transiting exoplanets, and the study of galaxy evolution using red-shifted spectra to beyond z = 10. The array cost, including delivery to the Moon by SpaceX Starship for installation using lunar base infrastructure, is around $10 billion, similar to that of the 25 m2 JWST. To test the concept, first a single prototype 6.5 m unit would be operated at the lunar south pole. This article is part of a discussion meeting issue 'Astronomy from the Moon: the next decades (part 2)'.

The Fate of the Interstellar Medium in Early-type Galaxies. III. The Mechanism of Interstellar Medium Removal and the Quenching of Star Formation

Understanding how galaxies quench their star formation is crucial for studies of galaxy evolution. Quenching is related to a decrease of cold gas. In the first paper we showed that the dust removal timescale in early-type galaxies (ETGs) is about 2.5 Gyr. Here we present carbon monoxide and 21 cm hydrogen line observations of these galaxies and measure the timescale of removal of the cold interstellar medium (ISM). We find that all the cold ISM components (dust and molecular and atomic gas) decline at similar rates. This allows us to rule out a wide range of potential ISM-removal mechanisms (including starburst-driven outflows, astration, or a decline in the number of asymptotic giant branch stars), and artificial effects like the stellar mass–age correlation, environmental influence, mergers, and selection bias, leaving ionization by evolved low-mass stars and ionization/outflows by Type Ia supernovae or active galactic nuclei as viable mechanisms. We also provide evidence for an internal origin of the detected ISMs. Moreover, we find that the quenching of star formation in these galaxies cannot be explained by a reduction in the gas amount alone, because the star formation rates (SFRs) decrease faster (on a timescale of about 1.8 Gyr) than the amount of cold gas. Furthermore, the star formation efficiency (SFE) of the ETGs ( SFE≡SFR/MH2 ) is lower than that of star-forming galaxies, whereas their gas mass fractions ( fH2≡MH2/M* ) are normal. This may be explained by the stabilization of gas against fragmentation, for example due to morphological quenching, turbulence, or magnetic fields.

Radio-astronomical image reconstruction with a conditional denoising diffusion model

Context. Reconstructing sky models from dirty radio images for accurate source extraction, including source localization and flux estimation, is a complex yet critical task, and has important applications in galaxy evolution studies at high redshift, particularly in deep extragalactic fields using for example the Atacama Large Millimetre Array (ALMA). With the development of large-scale projects, such as the Square Kilometre Array (SKA), we anticipate the need for more advanced source-extraction methods. Existing techniques, such as CLEAN and PyBDSF, currently struggle to effectively extract faint sources, highlighting the necessity for the development of more precise and robust methods. Aims. The success of the source-extraction process critically depends on the quality and accuracy of image reconstruction. As the imaging process represents an “information-lossy” operator, the reconstruction is characterized by uncertainty. The current study proposes the application of stochastic neural networks for the direct reconstruction of sky models from “dirty” images. This approach allows us to localize radio sources and to determine their fluxes with corresponding uncertainties, providing a potential advancement in the field of radio-source characterization. Methods. We used a dataset of 10 164 images simulated with the CASA tool based on the ALMA Cycle 5.3 antenna configuration. We applied conditional denoising diffusion probabilistic models (DDPMs) to directly reconstruct sky models from these dirty images, and then processed these models using Photutils to extract the coordinates and fluxes of the sources. To test the robustness of the proposed model, which was trained on a fixed water vapor value, we examined its performance under varying levels of water vapor. Results. We demonstrate that the proposed approach is state of the art in terms of source localisation, achieving over 90% completeness at a signal-to-noise ratio (S/N) of as low as 2. Additionally, the described method offers an inherent measure of prediction reliability thanks to the stochastic nature of the chosen model. In terms of flux estimation, the proposed model surpasses PyBDSF in terms of performance, accurately extracting fluxes for 96% of the sources in the test set, a notable improvement over the 57% achieved by CLEAN+ PyBDSF. Conclusions. Conditional DDPMs are a powerful tool for image-to-image translation, yielding accurate and robust characterization of radio sources, and outperforming existing methodologies. While this study underscores the significant potential of DDPMs for applications in radio astronomy, we also acknowledge certain limitations that accompany their use, and suggest directions for further refinement and research.